For those of you that have embarked on this journey, here are some key strategies to consider to ensure your efforts drive transformation:

Prioritize dashboards and analytics that have strategic impact - It's very easy for citizen data science programs to fizzle out if they are perceived as just the next enterprise effort to leverage the newest dashboard or reporting technology. To avoid this fate, prioritize efforts around some of the more strategic areas of growth or operational risk in the enterprise especially if they are high on a data-driven executive's radar. Citizen data science programs need to be highly visible initiatives to garner sufficient support if they are going to succeed in transforming more departments to be data-driven.

The job isn't done when you've delivered the dashboard - Dashboards, like any other tools or applications only deliver business value when they are used in business process and stakeholders agree to sunset legacy methods. In many cases, that means using your dashboards and foregoing the use of spreadsheets or performing other manual analysis. So before moving onto the next dashboards, citizen data scientists have to consider approaches to gain user adoption of their dashboards. This is far less trivial than it sounds especially when users demand that dashboards implement improvements a, b, and c before they start using them or inform you that they will continue to leverage legacy practices even when these dashboards are done. Citizen data scientists should plan to spend significant time with end users to illustrate how to use dashboards and analytics when performing specific business processes.

Create visual standards and establish a Center of Excellence (COE) - Self service BI tools are designed to help citizen data scientists to rapidly prototype and deliver new analytical dashboards. So, it's very easy for even a small group to push out many dashboards very quickly without considering the impact on users when dashboards are published without functional or visual standards. BI programs need to start with some basic visual standards, get agreement on their importance, and grow them over time.